AIMC Topic: Algorithms

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Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

International journal of paediatric dentistry
BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.

Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis.

Medical physics
BACKGROUND: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DB...

Improving Image Quality for Single-Angle Plane Wave Ultrasound Imaging With Convolutional Neural Network Beamformer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrafast ultrasound imaging based on plane wave (PW) compounding has been proposed for use in various clinical and preclinical applications, including shear wave imaging and super resolution blood flow imaging. Because the image quality afforded by ...

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to...

A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Sensors (Basel, Switzerland)
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical clou...

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

Sensors (Basel, Switzerland)
The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to com...

A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis.

Computational intelligence and neuroscience
Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time t...

Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA).

Environmental science and pollution research international
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO fixation...

Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

Towards understanding theoretical advantages of complex-reaction networks.

Neural networks : the official journal of the International Neural Network Society
Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks. This work takes one step on this direction by intro...